Dunn DC, Halpin PN

Rugosity-based regional modeling of hard-bottom habitat

Regional conservation planning efforts have led researchers and managers to investigate the use of remote sensing for identifying marine habitats that support high biodiversity. Dunn & Halpin have created the first data-driven regional model for predicting hard-bottom habitats from sea-bottom rugosity. Using logistic regression and Receiver Operator Characteristic (ROC) curves they predict the presence or absence of hard-bottom habitats on the upper shelf of the South Atlantic Bight. Their model offers a fast and inexpensive alternative to more traditional survey methods, and will be of value to regional conservation planners and fisheries managers.

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